UID:
edoccha_9960868942202883
Umfang:
1 online resource (496 pages)
Ausgabe:
1st ed.
ISBN:
1-00-333971-9
,
1-000-79120-3
,
1-003-33971-9
,
1-000-79504-7
,
87-7022-040-9
Serie:
River Publishers series in automation, control and robotics
Inhalt:
In today’s competitive global environment, manufacturers are offered with unprecedented opportunities to build hyper-efficient and highly flexible plants, towards meeting variable market demand, while at the same time supporting new production models such as make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO). During the last couple of years, the digital transformation of industrial processes is propelled by the emergence and rise of the fourth industrial revolution (Industry4.0). The latter is based on the extensive deployment of Cyber-Physical Production Systems (CPPS) and Industrial Internet of Things (IIoT) technologies in the manufacturing shopfloor, as well as on the seamless and timely exchange of digital information across supply chain participants. The benefits of Industry 4.0 have been already proven in the scope of pilot and production deployments in a number of different use cases including flexibility in automation, predictive maintenance, zero defect manufacturing and more. Despite early implementations and proof-of-concepts, CPPS/IIoT deployments are still in their infancy for a number of reasons, including:• Manufacturers’ poor awareness about digital manufacturing solutions and their business value potential, as well as the lack of relevant internal CPPS/IIoT knowledge.• The high costs that are associated with the deployment, maintenance and operation of CPPS systems in the manufacturing shopfloors, which are particularly challenging in the case of SME (Small Medium Enterprises) manufacturers that lack the equity capital needed to invest in Industry 4.0.• The time needed to implement CPPS/IIoT and the lack of a smooth and proven migration path from existing OT solutions.• The uncertainty over the business benefits and impacts of IIoT and CPPS technologies, including the lack of proven methods for the techno-economic evaluation of Industry4.0 systems. • Manufacturers’ increased reliance on external integrators, consultants and vendors. • The absence of a well-developed value chain needed to sustain the acceptance of these new technologies for digital automation.In order to alleviate these challenges, three European Commission funded projects (namely H2020 FAR-EDGE (http://www.far-edge.eu/), H2020 DAEDALUS (http://daedalus.iec61499.eu) and H2020 AUTOWARE (http://www.autoware-eu.org/)) have recently joined forces towards a “Digital Shopfloor Alliance”. The Alliance aims at providing leading edge and standards based digital automation solutions, along with guidelines and blueprints for their effective deployment, validation and evaluation. The present book provides a comprehensive description of some of the most representative solutions that offered by these three projects, along with the ways these solutions can be combined in order to achieve multiplier effects and maximize the benefits of their use. The presented solutions include standards-based digital automation solutions, following different deployment paradigms, such as cloud and edge computing systems. Moreover, they also comprise a rich set of digital simulation solutions, which are explored in conjunction with the H2020 MAYA project (http://www.maya-euproject.com/). The latter facilitate the testing and evaluation of what-if scenarios at low risk and cost, but also without disrupting shopfloor operations. As already outlined, beyond leading edge scientific and technological development solutions, the book comprises a rich set of complementary assets that are indispensable to the successful adoption of IIoT/CPPS in the shopfloor. The book is structured in three parts as follows: • The first part of the book is devoted to digital automation platforms. Following an introduction to Industry 4.0 in general and digital automation platforms in particular, this part presents the digital automation platforms of the FAR-EDGE, AUTOWARE and DAEDALUS projects. • The second part of the book focuses on the presentation of digital simulation and digital twins’ functionalities. These include information about the models that underpin digital twins, as well as the simulators that enable experimentation with these processes over these digital models. • The third part of the book provides information about complementary assets and supporting services that boost the adoption of digital automation functionalities in the Industry4.0 era. Training services, migration services and ecosystem building services are discussed based on the results of the three projects of the Digital Shopfloor Alliance. The target audience of the book includes:• Researchers in the areas of Digital Manufacturing and more specifically in the areas of digital automation and simulation, who wish to be updated about latest Industry4.0 developments in these areas.• Manufacturers, with an interest in the next generation of digital automation solutions based on Cyber-Physical systems.• Practitioners and providers of Industrial IoT solutions, which are interested in the implementation of use cases in automation, simulation and supply chain management.• Managers wishing to understand technologies and solutions that underpin Industry4.0, along with representative applications in the shopfloor and across the supply chain.
Anmerkung:
Front Cover -- Half Title -- RIVER PUBLISHERS SERIES IN AUTOMATION, CONTROL AND ROBOTICS -- Title Page - The Digital Shopfloor: Industrial Automation in the Industry 4.0 Era Performance Analysis and Applications -- Copyright -- Contents -- Foreword -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1 - Introduction to Industry 4.0 and the Digital Shopfloor Vision -- 1.1 Introduction -- 1.2 Drivers and Main Use Cases -- 1.3 The Digital Technologies Behind Industry 4.0 -- 1.4 Digital Automation Platforms and the Vision of the Digital Shopfloor -- 1.4.1 Overview of Digital Automation Platforms -- 1.4.2 Outlook Towards a Fully Digital Shopfloor -- 1.5 Conclusion -- References -- PART I -- Chapter 2 - Open Automation Framework for Cognitive Manufacturing -- 2.1 Introduction -- 2.2 State of the Play: Digital Manufacturing Platforms -- 2.2.1 RAMI 4.0 (Reference Architecture Model Industry 4.0) -- 2.2.2 Data-driven Digital Manufacturing Platforms for Industry 4.0 -- 2.2.3 International Data Spaces -- 2.3 Autoware Framework for Digital Shopfloor Automation -- 2.3.1 Digital Shopfloor Evolution: Trends & -- Challenges -- 2.3.1.1 Pillar 1: AUTOWARE open reference architecture for autonomous digital shopfloor -- 2.3.1.2 Pillar 2: AUTOWARE digital abilities for automatic awareness in the autonomous digital shopfloor -- 2.3.1.3 Pillar 3: AUTOWARE business value -- 2.3.2 AUTOWARE Software-Defined Autonomous Service Platform -- 2.3.2.1 Cloud & -- Fog computing services enablers and context management -- 2.3.3 AUTOWARE Framework and RAMI 4.0 Compliance -- 2.4 Autoware Framework for Predictive Maintenance Platform Implementation -- 2.4.1 Z-BRE4K: Zero-Unexpected-Breakdowns and Increased Operating Life of Factories -- 2.4.2 Z-Bre4k Architecture Methodology -- 2.4.3 Z-BRE4K General Architecture Structure.
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2.4.4 Z-BRE4K General Architecture Information Workflow -- 2.4.5 Z-BRE4K General Architecture Component Distribution -- 2.5 Conclusions -- References -- Chapter 3 - Reference Architecture for Factory Automation using Edge Computing and Blockchain Technologies -- 3.1 FAR-EDGE Project Background -- 3.2 FAR-EDGE Vision and Positioning -- 3.3 State of the Art in Reference Architectures -- 3.3.1 Generic Reference Architectures -- 3.3.2 RAMI 4.0 -- 3.3.3 IIRA -- 3.3.4 OpenFog RA -- 3.4 FAR-EDGE Reference Architecture -- 3.4.1 Functional Viewpoint -- 3.4.1.1 Automation domain -- 3.4.1.2 Analytics domain -- 3.4.1.3 Simulation domain -- 3.4.1.4 Crosscutting functions -- 3.4.2 Structural Viewpoint -- 3.4.2.1 Field Tier -- 3.4.2.2 Gateway Tier -- 3.4.2.3 Ledger Tier -- 3.4.2.4 Cloud Tier -- 3.5 Key Enabling Technologies for Decentralization -- 3.5.1 Blockchain Issues -- 3.5.2 Permissioned Blockchains -- 3.5.3 The FAR-EDGE Ledger Tier -- 3.5.4 Validation use Cases -- 3.6 Conclusions -- References -- Chapter 4 - IEC-61499 Distributed Automation for the Next Generation of Manufacturing Systems -- 4.1 Introduction -- 4.2 Transition towards the Digital Manufacturing Paradigm: A Need of the Market -- 4.3 Reasons for a New Engineering Paradigm in Automation -- 4.3.1 Distribution of Intelligence is Useless without Appropriate Orchestration Mechanisms -- 4.3.2 Defiance of Rigid Hierarchical Levels towards the Full Virtualization of the Automation Pyramid -- 4.4 IEC-61499 Approach to Cyber-Physical Systems -- 4.4.1 IEC-61499 runtime -- 4.4.2 Functional Interfaces -- 4.4.2.1 IEC-61499 interface -- 4.4.2.2 Wireless interface -- 4.4.2.3 Wrapping interface -- 4.4.2.4 Service-oriented interface -- 4.4.2.5 Fieldbus interface(s) -- 4.4.2.6 Local I/O interface -- 4.5 The "CPS-izer", a Transitional Path towards Full Adoption of IEC-61499 -- 4.6 Conclusions -- References.
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Chapter 5 - Communication and Data Management in Industry 4.0 -- 5.1 Introduction -- 5.2 Industry 4.0 Communication and Data Requirements -- 5.3 Industrial Wireless Network Architectures -- 5.4 Data Management in Industrial Environments -- 5.5 Hierarchical Communication and Data Management Architecture for Industry 4.0 -- 5.5.1 Heterogeneous Industrial Wireless Network -- 5.5.2 Hierarchical Management -- 5.5.2.1 Hierarchical communications -- 5.5.2.2 Data management -- 5.5.3 Multi-tier Organization -- 5.5.4 Architectural Enablers: Virtualization and Softwarization -- 5.5.4.1 RAN slicing -- 5.5.4.2 Cloudification of the RAN -- 5.6 Hybrid Communication Management -- 5.7 Decentralized Data Distribution -- 5.7.1 Average Data Access Latency Guarantees -- 5.7.2 Maximum Data Access Latency Guarantees -- 5.7.3 Dynamic Path Reconfigurations -- 5.8 Communications and Data Management within the AUTOWARE Framework -- 5.9 Conclusions -- References -- Chapter 6 - A Framework for Flexible and Programmable Data Analytics in Industrial Environments -- 6.1 Introduction -- 6.2 Requirements for Industrial-scale Data Analytics -- 6.3 Distributed Data Analytics Architecture -- 6.3.1 Data Routing and Preprocessing -- 6.3.2 Edge Analytics Engine -- 6.3.3 Distributed Ledger -- 6.3.4 Distributed Analytics Engine (DA-Engine) -- 6.3.5 Open API for Analytics -- 6.4 Edge Analytics Engine -- 6.4.1 EA-Engine Processors and Programmability -- 6.4.2 EA-Engine Operation -- 6.4.3 Configuring Analytics Workflows -- 6.4.4 Extending the Processing Capabilities of the EA-Engine -- 6.4.5 EA-Engine Configuration and Runtime Example -- 6.5 Distributed Ledger and Data Analytics Engine -- 6.5.1 Global Factory-wide Analytics and the DA-Engine -- 6.5.2 Distributed Ledger Services in the FAR-EDGE Platform -- 6.5.3 Distributed Ledger Services and DA-Engine.
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6.6 Practical Validation and Implementation -- 6.6.1 Open-source Implementation -- 6.6.2 Practical Validation -- 6.6.2.1 Validation environment -- 6.6.2.2 Edge analytics validation scenarios -- 6.6.2.3 (Global) distributed analytics validation scenarios -- 6.7 Conclusions -- References -- Chapter 7 - Model Predictive Control in Discrete Manufacturing Shopfloors -- 7.1 Introduction -- 7.1.1 Hybrid Model Predictive Control SDK -- 7.1.2 Requirements -- 7.1.3 Hybrid System -- 7.1.4 Model Predictive Control -- 7.2 Hybrid System Representation -- 7.2.1 Piece-Wise Affine (PWA) System -- 7.2.2 Mixed Logical Dynamical (MLD) System -- 7.2.3 Equivalence of Hybrid Dynamical Models -- 7.3 Hybrid Model Predictive Control -- 7.3.1 State of the Art -- 7.3.2 Key Factors -- 7.3.3 Key Issues -- 7.4 Identification of Hybrid Systems -- 7.4.1 Problem Setting -- 7.4.2 State-of-the-Art Analysis -- 7.4.3 Recursive Two-Stage Clustering Approach -- 7.4.4 Computation of the State Partition -- 7.5 Integration of Additional Functionalities to the IEC 61499 Platform -- 7.5.1 A Brief Introduction to the Basic Function Block -- 7.5.2 A Brief Introduction to the Composite Function Block -- 7.5.3 A Brief Introduction to the Service Interface Function Block -- 7.5.4 The Generic DLL Function Block of nxtControl -- 7.5.5 Exploiting the FB DLL Function Block as Interfacing Mechanism between IEC 61499 and External Custom Code -- 7.6 Conclusions -- References -- Chapter 8 - Modular Human-Robot Applications in the Digital Shopfloor Based on IEC-61499 -- 8.1 Introduction -- 8.2 Human and Robots in Manufacturing: Shifting the Paradigm from Co-Existence to Mutualism -- 8.3 The "Mutualism Framework" Based on IEC-61499 -- 8.3.1 "Orchestrated Lean Automation": Merging IEC-61499 with the Toyota Philosophy -- 8.3.2 A Hybrid Team of Symbionts for Bidirectional Mutualistic Compensation.
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8.3.3 Three-Dimensional Characterization of Symbionts' Capabilities -- 8.3.4 Machine Learning Applied to Guarantee Dynamic Adherence of Models to Reality -- 8.4 Technological Approach to the Implementation of Mutualism -- 8.4.1 "Mutualism Framework" to Sustain Implementation of Symbionts-Enhanced Manufacturing Processes -- 8.4.2 IEC-61499 Engineering Tool-Chain for the Design and Deployment of Real-Time Orchestrated Symbionts -- 8.4.3 AI-Based Semantic Planning and Scheduling of Orchestrated Symbionts' Tasks -- 8.4.4 Modular Platform for Perceptual Learning and Augmentation of Human Symbionts -- 8.4.5 Training Gymnasium for Progressive Adaptation andPerformance Improvement of Symbionts' Mutualistic Behaviours -- 8.5 The Potential to Improve Productivity and the Impact this Could Have on European Manufacturing -- 8.6 Conclusions -- References -- PART II -- Chapter 9 - Digital Models for Industrial Automation Platforms -- 9.1 Introduction -- 9.2 Scope and Use of Digital Models for Automation -- 9.2.1 Scope of Digital Models -- 9.2.2 Factory and Plant Information Modelling -- 9.2.3 Automation and Analytics Processes Modelling -- 9.2.4 Automation and Analytics Platforms Configuration -- 9.2.5 Cyber and Physical Worlds Synchronization -- 9.2.6 Dynamic Access to Plant Information -- 9.3 Review of Standards Based Digital Models -- 9.3.1 Overview -- 9.3.2 IEC 62264 -- 9.3.3 IEC 62769 (FDI) -- 9.3.4 IEC 62453 (FDT) -- 9.3.5 IEC 61512 (Batch Control) -- 9.3.6 IEC 61424 (CAEX) -- 9.3.7 Business to Manufacturing Markup Language (B2MML) -- 9.3.8 AutomationML -- 9.4 FAR-EDGE Digital Models Outline -- 9.4.1 Scope of Digital Modelling in FAR-EDGE -- 9.4.2 Main Entities of Digital Models for Data Analytics -- 9.4.3 Hierarchical Structure -- 9.4.4 Model Repository Open Source Implementation -- 9.5 Simulation and Analytics Models Linking and Interoperability.
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9.6 Conclusions.
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English
Weitere Ausg.:
ISBN 87-7022-041-7
Sprache:
Englisch
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